Performance Evaluation of Methods for Correcting Ocular Artifacts

in Electroencephalographic (EEG) Recordings

Murielle Kirkove, Clémentine François, Aurélie Libotte and Jacques G. Verly

Department of Electrical Engineering and Computer Science

University of Liège, Grande Traverse 10, B 4000 Liège, Belgium

Keywords: Electroencephalography, Ocular Artifact, Wavelet Transform, Adaptive Filtering, Blind Source Separation.

Abstract: The presence of ocular artifacts (OA) due to eye movements and eye blinks is a major problem for the

analysis of electroencephalographic (EEG) recordings in most applications. A large variety of methods

(algorithms) exist for detecting or/and correcting OA’s. We identified the most promising methods,

implemented them, and compared their performance for correctly detecting the presence of OA’s. These

methods are based on signal processing “tools” that can be classified into three categories: wavelet

transform, adaptive filtering, and blind source separation. We evaluated the methods using EEG signals

recorded from three healthy persons subjected to a driving task in a driving simulator. We performed a

thorough comparison of the methods in terms of the usual performances measures (sensitivity, specificity,

and ROC curves), using our own manual scoring of the recordings as ground truth. Our results show that

methods based on adaptive filtering such as LMS and RLS appear to be the best to successfully identify

OA’s in EEG recordings.

1 INTRODUCTION

Electroencephalographic (EEG) recordings reflect

the neuronal and electrical activity within the brain.

They are obtained from electrodes placed on the

scalp. They are often contaminated by signals from

other sources, called artifacts. (Artifact is also used

to denote the local deformation of the signal of

interest, here the EEG.) One distinguishes between

physiological artifacts and technical artifacts. The

most frequent physiological artifacts are due to the

activity of the eyes, the heart, and the muscles. The

most common physiological artifacts are the ocular

artifacts (OA’s), due to the movements of the

eyeballs and eyelids. Technical artifacts are mostly

due to electrode placement problems and body

movements.

All artifacts result in an EEG recording that may

be quite different, generally locally, from the true

underlying EEG signal reflecting the brain activity.

It is thus critical to do something about OA’s.

The three usual ways of dealing with OA’s are

prevention, rejection, and removal. Prevention

consists in reducing the occurrences of OA’s by

giving proper instructions to patients. However,

some OA’s are involuntary and unavoidable.

Rejection consists in rejecting the epochs affected by

OA’s. Of course, rejection implies that the OA’s be

first detected. Although simple, rejection has the

major drawback of dropping a significant amount of

valuable data. Removal consists in removing as best

as possible the OA’s to produce a signal that is as

close as possible to the true, underlying EEG signal.

Removal may require that the OA’s be first detected.

Since removing the OA’s corrects the signals, the

term “correction” can also be used in place of the

term “removal”. Any correction method can be

turned into a detection method by thresholding the

difference between the raw signal and the cleaned

one.

When dealing with OA’s, it is useful to record

the electrooculographic signals (EOG), which allow

the observer (and the algorithms) to establish a

“correlation” between the OA’s in the EEG and the

features in the EOG.

Our interest in the handling of OA’s arose from

the study of drowsiness for subjects actively

involved in a task, such as driving. Indeed, until they

fall asleep, these subjects have their eyes mostly

open. Therefore, the EEG signals recorded for

studying the evolution of drowsiness are affected by

OA’s due to eye movements and eye blinks. This

126

Kirkove M., François C., Libotte A. and G. Verly J..

Performance Evaluation of Methods for Correcting Ocular Artifacts in Electroencephalographic (EEG) Recordings.

DOI: 10.5220/0004199101260132

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2013), pages 126-132

ISBN: 978-989-8565-36-5

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

should be contrasted with the study of sleep, where

subjects have their eyes closed. (However, note that

the eyes and the eyelids can move even when the

eyes are closed.)

Several methods have been proposed in the

literature for cleaning EEG’s from OA’s.

Comprehensive reviews are found in (Croft and

Barry 2000) and (Kandaswamy et al. 2005).

However, we have not found any published paper

comparing a significant number of the proposed

methods in terms of a common performance

measure. The present paper performs such a

comparison.

2 MATERIAL AND METHODS

2.1 Data Recordings

We acquired data at the “Centre d’Etudes des

Troubles de l’Eveil et du Sommeil” (CETES) of the

University Hospital of Liège in the context of the

study of driver drowsiness. Subjects were presented

with a driving task in a simulator. We recorded the

following polysomnographic (PSG) signals: EEG

(for electrodes Fz, Cz, Pz, C3, C4, A1, A2), EOG,

and EMG. The subjects received the instruction to

drive at a constant speed of 80 km/h on a one-way

road, where there were no other vehicles. This task

lasted about two hours. The PSG signals were

recorded with an Embla system at a sampling rate of

500 Hz. They were partitioned into butting (and thus

non-overlapping) epochs of 1024 samples. The

methods described below, except the last one, were

successively applied to each of these epochs. The

last method was applied on one whole EEG

recording.

2.2 Methods Compared

We identified 12 potentially useful methods in the

literature. We organized these methods according to

the seven signal processing “tools” they use (DWT,

SWT, LMS, RLS, H

∞

-TV, ICA, SOBI), which we

further organized into three broad categories (of

tools), i.e. wavelet transform (WT), adaptive

filtering (AF), and blind source separation (BSS)

tools. The abbreviations are spelled out below. Table

1 shows the tools used by the 12 methods. For

example, Method 4 uses both the SWT and LMS

tools.

Table 1 shows that Methods 1 and 2 use only

WT tools, that Methods 3, 5, and 7 use only AF

tools, and that all BSS tools are used in combination

with WT tools. Methods 4, 6, and 8 - 12 use two

tools, each from a different category.

Table 1: Methods compared, and the “tools” they use.

Methods

Tools

WT AF BSS

DWT

SWT

LMS

RLS

∞

-TV

ICA

SOBI

We now successively consider the broad

categories (WT, AF, BSS) of tools, and, for each, we

provide the description of the methods that use these

tools. These descriptions generally do not refer

explicitly to the method indices of Table 1.

2.2.1 Wavelet Transform (WT) Tools

The wavelet transform (WT) (Mallat 1999) is one of

the leading techniques for analyzing non-stationary

signals like EEG’s. The major asset of wavelet

analysis is its capability to decompose waveforms

into components that are well localized in time and

in frequency (or, equivalently, in scale).

The continuous WT (CWT) constructs a

“family” of wavelets by scaling and translating a

function called the mother wavelet.

The discrete WT (DWT) results from the

discretization of the CWT on a dyadic grid.

Translation invariance is important in many

applications such as change detection and denoising.

The stationary WT (SWT) is a WT algorithm

designed to overcome the lack of translation

invariance of the DWT (Nason and Silverman

1995). Translation invariance is achieved by

removing the down-samplers and up-samplers

present in the DWT.

2.2.1.1 Detection of OA’s with DWT

Krishnaveni et al. applied a wavelet-based

thresholding algorithm to identify zones of OA’s

(Krishnaveni et al. 2006). They based their method

on (Venkataramanan et al. 2004), i.e. they used the

Haar wavelet to precisely detect the moment when

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the state of the eye changes from open to closed and

vice versa.

The technique is based on the difference in

frequency contents between the EEG recording ([0-

20] Hz) and the OA signals ([0-16] Hz). The raw

EEG signal is decomposed with the Haar DWT. The

detail wavelet coefficients (WCf’s) are then

cancelled and this results in a step function with a

falling edge indicating a change from open to closed

eyes, or with a rising edge indicating a change from

closed to open eyes.

The edges of the approximation are classified

into artifact or non-artifact edges according to their

relative amplitude.

2.2.1.2 Correction of OA’s with SWT

Krishnaveni et al. consider the OA’s as a noise part

of the EEG recording, and they apply a wavelet-

based thresholding algorithm to remove them

(Krishnaveni et al. 2006). Soft-thresholding is the

most popular thresholding technique, and it has been

theoretically justified by Donoho and Johnstone.

These last authors suggest to choose optimal

thresholds by minimizing the Stein Unbiased Risk

Estimator (SURE) at each decomposition level

(Donoho and Johnstone 1995).

Soft-thresholding functions are continuous with

discontinuous derivatives. However, continuous

derivatives of first and higher orders are often

desired for optimization problems. A new class of

soft-like-thresholding functions with continuous

derivatives was proposed (Xiao-Ping and Desai

1998). The method consists in applying the SWT

with Coiflet3 as mother wavelet for levels 3 to 6,

selecting the optimal threshold for each level by

minimizing the SURE, applying soft-like-

thresholding, and applying the inverse SWT.

Since OA’s occupy the lower frequency band

([0-16] Hz) of the typical EEG, the threshold

selection and the thresholding are only performed on

the decomposition levels 3 to 6. Coiflet3 is chosen

as the mother wavelet since it resembles the shape of

an eye-blink OA. This implies that large WCf’s be

generated in OA zones and that small WCf’s be

generated in areas corresponding to non-OA zones.

Reducing the amplitude range of the large

coefficients should then result in the removal or

reduction of the OA’s.

2.2.2 Adaptive Filtering (AF) Tools

Adaptive filters (AF’s) belong to the category of

optimal filters (Klados et al. 2009; Correa and Leber

2011): they adapt their coefficients to the

disturbance in the input signal, and subtract the

result from the input signal. The adaptive process

involves an optimization controlled by the error

signal between the input signal and the filter-output

signal. We tested three AF algorithms: (1) the least

mean square (LMS) algorithm, which minimizes the

mean squared error, (2) the recursive least squares

(RLS) algorithm, which minimizes a cost function

that is a linear combination of squared errors, and

(3) the H

∞

Time-Varying (H

∞

-TV) algorithm, which

minimizes the infinite norm of a linear combination

of squared errors (Puthusserypady and Ratnarajah

2006).

We implemented these three AF’s as presented

in Tables 1-3 of (Klados et al. 2009).

The application of AF’s can be combined with

the use of the SWT (Kumar et al. 2008). The

procedure consists in applying the SWT with the

Symlet3 mother wavelet up to eight levels, applying

the AF to the WCf’s, and applying the inverse SWT

to the error signal.

2.2.3 Blind Source Separation (BSS) Tools

Blind source separation (BSS) techniques are based

on a linear decomposition of the measured signals

into sources, also called components. Applied to

EEG and EOG recordings, these methods segregate

the artifactual activities into separate sources.

Therefore, the reconstruction of the recorded EEG

with these sources removed leads to a reduction of

OA’s. These techniques can be used with several

EEG channels.

The most common BSS methods are the

independent component analysis (ICA) and the

second-order blind identification (SOBI).

ICA is a statistical technique in which measured

signals are linearly transformed into sources that are

maximally independent from each other (Hyvärinen

and Oja 2000).

Numerous ICA algorithms exist. FastICA and

Infomax are the most popular ones. Infomax (Bell

and Sejnowski 1995) is effective in separating

sources that have super-Gaussian probability density

functions, but it fails to separate sources that have

negative Kurtosis. Unless explicitly stated otherwise,

we have used FastICA.

SOBI (Belouchrani et al. 2002) divides a set of

measured signals into sources by exploiting the

possible time coherence between the sources. It

minimizes the cross-correlations between each

component and other components shifted in time,

across a set of time delays.

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2.2.3.1 Correction of OA’s by Combining a BSS

Tool with High-order Statistics

The two methods we describe here are based on the

same scheme. The first one is that of (Ghandeharion

and Erfanian 2010) where the BSS tool is ICA. The

second one uses SOBI instead of ICA.

The methods first decompose the EEG and EOG

recordings (two channels) into sources, by applying

either of the BSS transforms. They then identify the

artifactual source (in the way described below) and

remove it. They finally produce the output signal by

applying the appropriate inverse transform to the

remaining (non-artifactual) sources.

The artifactual source is identified as follows.

For each of the above sources, one computes seven

statistical measures, with four directly on the sources

and one on each set of SWT coefficients for levels 3

to 5. The four measures on each source are (1) the

mutual information, (2) the projection strength, (3)

the correlation, and (4) the kurtosis. The measure on

the selected SWT coefficients is the kurtosis. One

then flags for each measure the couple

source/measure with maximum measure values. Any

source with four flags is deemed to be artifactual.

2.2.3.2 Correction of OA’s by Simultaneously

using ICA and DWT

The main drawback of ICA is that the number of

measured signals must be larger than the number of

sources for correctly decomposing the different

types of artifacts. Therefore, ICA has difficulty in

separating the OA sources from the true PSG

sources. Moreover, the spectrum of some OA’s is

located in a narrow frequency band. Since ICA

works in the time domain and since DWT has a

good frequency resolution, the combination of ICA

and DWT is particularly well adapted.

Automatic wavelet independent component

analysis (AWICA) (Mammone et al. 2012)

combines DWT and ICA on multichannel PSG

recordings to improve the performance of source

separation. This method consists in the six following

phases executed on each epoch:

 Each recorded PSG channel is decomposed by

DWT with the Daubechies4 mother wavelet.

The four frequency bands characterizing the

brain activity are represented by the wavelet

components (WC’s).

 An automatic procedure is applied to measure

the level of “artifactuality” of the WC’s. Two

measures are used to this end: the kurtosis

(Kt) and the Renyi’s entropy (ReE). This last

measure allows one to quantify the

randomness. The Kt and the ReE of the WC’s

are computed and then normalized to zero

mean and unit variance with respect to every

WC. If one of these normalized measures

exceeds a fixed threshold, the WC is marked

as being a critical wavelet component (CWC).

 ICA is applied to all CWC’s. The critical

wavelet independent components (CWIC’s)

are so extracted.

 The set of CWIC’s is partitioned into non-

overlapping windows. If the Kt or the ReE of

one CWIC exceeds a fixed threshold in more

than 20% of the non-overlapping windows, it

is marked and rejected.

 An inverse ICA is applied so that artifact-free

WC’s are recovered.

 The inverse DWT is applied to reconstruct the

cleaned EEG signals (channels).

2.2.3.3 Correction of OA’s by Combining ICA

and Wavelet Denoising in a Robust Way

The method called Robust Artifact Removal (RAR)

is presented in (Zima et al. 2012) as a method for

removing short-duration, high-amplitude artifacts

from long-term neonatal EEG recordings.

It consists in three major phases: (1) partitioning

the EEG recording (one channel) into contiguous

epochs in three different ways; (2) independent

processing (as described below) of each partition;

(3) combining the three artifact-free reconstructions

for obtaining a reconstruction that is freer of

artifacts.

Phase (2) consists of five processing steps: (1)

ICA, (2) artifact detection, (3) wavelet denoising of

artifact sources by using DWT and soft-

thresholding, (4) replacement of the artifact sources

by their noise part, estimated in previous step, (5)

inverse ICA.

For ICA, we use the implementation of

(Tichavský and Yeredor 2009) of the algorithm

BGSEP (Pham and Cardoso 2001). This algorithm is

based on second-order statistics as in the SOBI

algorithm, but uses the non-stationarity of the

measured signals.

The identification of high-amplitude artifact

sources is based on their duration, which is short in

comparison to the partition length. The authors call

such sources “sparse” in the time domain. They

define the sparsity of a signal as a value proportional

to its maximum amplitude and logarithmically

proportional to the inverse of its median. A source

with sparsity exceeding a fixed threshold is marked

as an artifact.

The specific combination of the three

reconstructions, called “adaptive folding”, allows

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129

one to reduce the possible remaining artifacts by

averaging, epoch-by-epoch, the reconstructions

containing the fewest artifacts. The presence, or not,

of artifacts is decided based upon the differences and

the maximum absolute values of the reconstructions.

2.3 Method of Performance Evaluation

For memory, Method 1 is a detection method, and

all others are correction methods. No obvious

evaluation method is available for estimating the

performance of a correction method. Indeed, we do

not have an accurate means of measuring the true

EEG signal. For this reason and for the purpose of

evaluating the performance of the methods, we

decided to “turn” the 11 correction methods into

detection methods. This transformation is done by

subtracting the corrected EEG signal from the raw

EEG signal and thresholding the result.

To quantify the detection performance of the 12

methods, we defined the ground truth by manually

segmenting many 2s epochs of 1024 samples each

into true OA zones and true non-OA zones. For this,

we used a tool included in the Matlab toolbox

Fieldtrip (Oostenveld et al. 2011).

The top part of Fig. 1 illustrates the “true”

segmentation of, say, one epoch performed manually

by an observer into OA zones and non-OA (OA



)

zones. The bottom part illustrates the corresponding

“computed” segmentation performed automatically

by some method. The boundaries of the true and

computed zones define intervals that can each be

labeled as true positive (tp), true negative (tn), false

positive (fp), and false negative (fn). We transform

this labeling into the customary tp, tn, fp, and fn

numbers by simply adding the lengths of the

intervals that have the same, corresponding label.

These four numbers define a confusion matrix.

However, the fundamental measures of performance

that we use to compare the 12 methods are:

 The tp rate, which is the ratio between tp and

the number of positives, i.e. tp + fp;

 The fp rate, which is the ratio between fp and

the number of negatives, i.e. tn + fn.

The tp rate is also called the sensitivity and “1- the

fp rate” the specificity. We use the common receiver

operating characteristic (ROC) curves for

representing these measures.

Figure 1: Evaluation: segmentations into true (top) and

computed (bottom) OA zones and non-OA (OA



) zones.

3 RESULTS

Figure 2 shows the results of the 12 methods on one

epoch of 1024 samples from one EEG recording.

Figure 2: Results of the 12 methods on one epoch of 1024

samples from one EEG recording. The thin (thick) lines

show the raw (cleaned) EEG signals.

Method 1 detects correctly the OA zone.

Method 2 is not capable of correcting EEG

signals for OA’s. This observation is in

contradiction with the results presented in

(Krishnaveni et al. 2006). Our conclusion is that this

method should not be expected to work because the

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method is one of denoising, and therefore applicable

only to white noise. However, OA’s cannot be

considered to be white noise! Therefore, we decided

to ignore this method in our performance evaluation.

The results of the LMS and RLS methods (Methods

3 and 6) are very similar: the spike due to the OA is

weakened. In the results of H

∞

-TV (Methods 7 and

8), the OA spike is clearly reduced, but useful data is

also perturbed. The results of the BSS methods

(Methods 9 to 12) are quite similar: the OA peak has

disappeared.

Figure 3 shows the ROC curves of the 11

retained methods on the same EEG recording (i.e.

with Method 2 ignored). The four best ROC curves

are given by the LMS and RLS methods (Methods 3

to 6).

Figure 3: ROC curves for the 11 retained methods.

The sensitivity and the specificity have

antagonistic behaviors. Therefore, another way of

comparing the performances of the methods is to

consider the sum of the sensitivity and the

specificity. Then, the larger the sum is, the better the

performance is. Table 2 lists the sensitivity, the

specificity, and their sum. The rows of the four best

methods are shown in gray with the performance

increasing from light to dark gray.

4 DISCUSSION

Method 1, which is a detection method based on a

thresholding of wavelet approximation coefficients,

does not seem to correctly identify all OA zones in

the considered EEG recording (in comparison to the

reference). Indeed, Method 1 has one of the lowest-

positioned ROC curve in Figure 3. In addition, we

see from Table 2 that the sensitivity barely reaches

0.275. This means that only 27.5% of OA’s are

correctly detected. However, the method has a high

specificity.

Figure 2 shows that all other methods – which

are correction methods –, except for Method 2, are

able to remove a substantial amount of OA from the

EEG recording. In each graph of this figure (except

for that of Method 2), one can observe that the spike

due to the OA is clearly reduced. However, as

indicated earlier, it is difficult to evaluate the

performance of the correction methods because we

cannot measure directly the activity of the brain and

of the eyes separately. We will thus discuss the

results of these methods of correction in terms of

their ability to identify correctly the OA zones in the

EEG recording.

In general, methods based on adaptive filtering

show better results than those based on BSS

methods. Indeed, Table 2 indicates that the sum of

the values of sensitivity and specificity is higher for

Methods 3 to 8 than for Methods 9 to 12. This is

confirmed by the ROC curves shown in Figure 3,

where one can observe that the curves for Methods 3

to 8 are located closer to the upper-left corner than

those for Methods 9 to 12. Table 2 and Figure 3

indicate that Methods 7 and 8 can correctly identify

the OA zones. However, visual inspection of the

corresponding graphs of Figure 2 reveals that these

methods also remove a lot of useful data. Methods 3

to 6 (LMS- and RLS-based algorithms) are thus the

four best methods to successfully identify OA zones

in the EEG recording.

From Table 2 and Figure 3, one can also

conclude that combining the LMS and RLS

algorithms with the SWT does not improve the

results as compared to using LMS and RLS alone.

Table 2: Best compromise in sensitivity and specificity for

the 11 retained methods.

Methods

ensitivi

eci

icit

ens.+ s

ec.

Method 1 0.275 0.985 1.260

Method 3 0.791 0.768 1.559

Method 4 0.717 0.813 1.530

Method 5 0.642 0.858 1.500

Method 6 0.647 0.847 1.494

Method 7 0.587 0.882 1.469

Method 8 0.578 0.882 1.460

Method 9 0.639 0.775 1.414

Method 10 0.641 0.828 1.469

Method 11 0.123 0.956 1.079

Method 12 0.501 0.779 1.280

5 CONCLUSIONS

Ocular artifacts (OA’s) are often present in EEG

recordings. They mask the true, underlying EEG

signal. As a result, the OA’s make the analysis of

EEG recordings more difficult and, more

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importantly, they can lead to incorrect analysis and

wrong conclusions. To avoid losing valuable data, it

is critical to develop robust methods for cleaning out

EEG recordings from OA’s. For the purpose of

evaluating the state of the art in the detection and

elimination/reduction of OA’s, we implemented 12

promising methods found in the literature. We

evaluated the performance of all the methods in

terms of their ability to correctly detect OA zones in

EEG recordings, as compared to a ground truth

established visually. Results suggest that methods

based on adaptive filtering such as LMS and RLS, as

well as their combination with the SWT are the best

methods to successfully detect OA zones in EEG

recordings. These methods have higher values of

sensitivity and specificity, and better ROC curves,

than the other correction methods.

ACKNOWLEDGEMENTS

The authors thank IFSTTAR for making available

one of their driving simulator software, and the

“Centre d’Etudes des Troubles de l’Eveil et du

Sommeil” (CETES) for making available their

facilities and equipments.

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